IJCAI 2017 Accepted Tutorials and Schedule

Half Day Tutorials

T1. Argumentation in Artificial Intelligence: From Theory to Practice
Federico Cerutti, Mauro Vallati
T2.IoT Big Data Stream Mining
Joao Gama, Gianmarco de Francisci Morales, Latifur Khan, Wei Fan, Albert Bifet
T3. Interactive Machine Learning: From Classifiers to Robotics
Ece Kamar, Bradley Hayes , Matthew Taylor
T4. Multimodal Learning and Reasoning
Angeliki Lazaridou, Desmond Elliott, Douwe Kiela
T5. Acquisition, Representation and Usage of Conceptual Hierarchies
Marius Pasca
T6. Computational Models for Social Influence and Diffusion
Yang Yang, Jie Tang
T7. Energy-based machine learning
Takayuki Osogami, Sakyasingha Dasgupta
T8. Declarative Spatial Reasoning: Theory, Methods, and Applications
Carl Schultz, Przemysław Wałęga, Mehul Bhatt
T9. Data Mining and Machine Learning using Constraint Programming Languages
Siegfried Nijssen, Tias Guns, Ian Davidson
T10. Markov Logic Networks: Recent Advances and Practical Applications
Deepak Venugopal, Vincent Ng, Vibhav Gogate
T11. Machine learning for dynamic social network analysis
Manuel Gomez Rodrieguez
T12. Learning and Decision-Making from Rank Data
Lirong Xia
T13. Theory and practice of revenue optimal mechanism design
Pingzhong Tang, Zihe Wang
T14. Multiwinner Elections: Applications, Axioms, and Algorithms
Piotr Faliszewski and Piotr Skowron
T15. Deep Reinforcement Learning Through Policy Optimization
Aviv Tamar, John Schulman, Pieter Abbeel
T16. Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design
Holger Hoos, Frank Hutter
T17. Multiagent Learning: Foundations and Recent Trends
Stefano V. Albrecht, Peter Stone

Quarter Day Tutorials

T18. Unifying Logic, Dynamics and Probability: Foundations, Algorithms and Challenges
Vaishak Belle
T19. Theoretical Analysis of Policy Iteration
Shivaram Kalyanakrishnan
T20. First-Order Multi-agent Logics in Action
Vaishak Belle
T21. Heterogeneous Learning: Recent Advance and Future Studies
Jingrui He
T22. Strategic Voting and AI
Reshef Meir
T23. Strategic Voting and Strategic Candidacy in Multi-Agent Systems
Maria Polukarov, Svetlana Obraztsova, Zinovi Rabinovich

Tutorial Schedule

Saturday
Room A
SaA1 (0.5) IoT Big Data Stream Mining
SaA2 (0.5) Data Mining and Machine Learning using Constraint Programming Languages
Room B
SaB1 (0.5) Multiagent Learning: Foundations and Recent Trends
SaB2 (0.5) Theory and practice of revenue optimal mechanism design
Room C
SaC1 (0.5) Computational Models for Social Influence and Diffusion
SaC2 (0.5) Machine learning for dynamic social network analysis
Sunday
Room A
SuA1 (0.5) Interactive Machine Learning: From Classifiers to Robotics
SuA2 (0.5) Deep Reinforcement Learning Through Policy Optimization
Room B
SuB1 (0.5) Multiwinner Elections: Applications, Axioms, and Algorithms
SuB2 (0.25) Strategic Voting and AI
SuB3 (0.25) Strategic Voting and Strategic Candidacy in Multi-Agent Systems
Room C
SuC1 (0.5) Declarative Spatial Reasoning: Theory, Methods, and Applications
SuC2 (0.25) Unifying Logic, Dynamics and Probability: Foundations, Algorithms and Challenges
SuC3 (0.25) First-Order Multi-agent Logics in Action
Monday
Room A
MA1 (0.5) Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design
MA2 (0.25) Theoretical Analysis of Policy Iteration
MA3 (0.25) Heterogeneous Learning: Recent Advance and Future Studies
Room B
MB1 (0.5) Argumentation in Artificial Intelligence: From Theory to Practice
SuB2 (0.5) Acquisition, Representation and Usage of Conceptual Hierarchies
Room C
MC1 (0.5) Markov Logic Networks: Recent Advances and Practical Applications
MC2 (0.5) Energy-based machine learning
Room D
MD1 (0.5) Learning and Decision-Making from Rank Data